Sleep monitoring using a photoplethysmograph

ABSTRACT

A method for diagnosis includes receiving from a sensor coupled to a body of a sleeping patient a photoplethysmograph signal. The photoplethysmograph signal is processed independently of any other physiological measurements in order to identify sleep stages of the patient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part of PCT patent applicationPCT/IL2005/001233, filed Nov. 22, 2005, which is a Continuation of Ser.No. 10/995,817, filed Nov. 22. 2004 now U.S. Pat. No. 7,578,793 andwhich claims the priority of U.S. Provisional Patent Application60/843,107, filed Sep. 7, 2006, and is a Continuation-In-Part of PCTpatent application PCT/IL2006/000148, filed Feb. 7, 2006, claiming thepriority of U.S. Provisional Patent Application 60/651,295, filed Feb.7, 2005. This application is related to three other U.S. patentapplications, all filed on even date, which are entitled “Detection ofHeart Failure Using a Photoplethysmograph,” “Respiration-Based Prognosisof Heart Disease,” and “Sleep Monitoring Using a Photoplethysmograph.”The disclosure of all of these related applications are incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates generally to physiological monitoring anddiagnosis, and specifically to sleep recording and analysis.

BACKGROUND OF THE INVENTION

Human sleep is generally described as a succession of five recurringstages (plus waking, which is sometimes classified as a sixth stage).Sleep stages are typically monitored using a polysomnograph to collectphysiological signals from the sleeping subject, including brain waves(EEG), eye movements (EOG), muscle activity (EMG), heartbeat (ECG),blood oxygen levels (SpO2) and respiration. The commonly-recognizedstages include:

-   -   Stage 1 sleep, or drowsiness. The eyes are closed during Stage 1        sleep, but if aroused from it, a person may feel as if he or she        has not slept.    -   Stage 2 is a period of light sleep, during which the body        prepares to enter deep sleep.    -   Stages 3 and 4 are deep sleep stages, with Stage 4 being more        intense than Stage 3.    -   Stage 5, REM (rapid eye movement) sleep, is distinguishable from        non-REM (NREM) sleep by changes in physiological states,        including its characteristic rapid eye movements.        Polysomnograms show brain wave patterns in REM to be similar to        Stage 1 sleep. In normal sleep, heart rate and respiration speed        up and become erratic, while the muscles may twitch. Intense        dreaming occurs during REM sleep, but paralysis occurs        simultaneously in the major voluntary muscle groups.

Sleep apneas commonly occur in conjunction with a variety ofcardiorespiratory disorders. The relationship between sleep apnea andheart failure, for example, is surveyed by Bradley et al. in twoarticles entitled “Sleep Apnea and Heart Failure,” including “Part I:Obstructive Sleep Apnea,” Circulation 107, pages 1671-1678 (2003), and“Part II: Central Sleep Apnea,” Circulation 107, pages 1822-1826 (2003),which are incorporated herein by reference. The authors define “apnea”as a cessation of airflow for more than 10 sec. This term isdistinguished from “hypopnea,” which is a reduction in but not completecessation of airflow to less than 50% of normal, usually in associationwith a reduction in oxyhemoglobin saturation (commonly referred to as“desaturation”).

Sleep apneas and hypopneas are generally believed to fall into twocategories: obstructive, due to collapse of the pharynx; and central,due to withdrawal of central respiratory drive to the muscles ofrespiration. Central sleep apnea (CSA) is commonly associated withCheyne-Stokes respiration, which is a form of periodic breathing inwhich central apneas and hypopneas alternate with periods ofhyperventilation, with a waxing-waning pattern of tidal volume. CSA isbelieved to arise as the result of heart failure, though obstructivesleep apnea (OSA) may also occur in heart failure patients.

Both OSA and CSA increase the strain on the cardiovascular system andthus worsen the prognosis of the heart failure patient. In some cases,both types of apneas may occur in the same patient, even at the sametime (superposition). Classifying respiratory events as central orobstructive is considered to be a critical point, since treatment maydiffer according to the type of events, as pointed out by Pepin et al.in “Cheyne-Stokes Respiration with Central Sleep Apnea in Chronic HeartFailure Proposals for a Diagnostic and Therapeutic Strategy,” SleepMedicine Reviews 10, pages 33-47 (2006), which is incorporated herein byreference. Both CSA and OSA can be manifested in periodic breathingpatterns.

Various methods have been proposed in the patent literature forautomated apnea detection and diagnosis based on patient monitoringduring sleep. For example, U.S. Patent Application Publication US2004/0230105 A1 describes a method for analyzing respiratory signalsusing a Fuzzy Logic Decision Algorithm (FLDA). The method may be used toassociate respiratory disorders with obstructive apnea, hypopnea,central apnea, or other conditions. As another example, U.S. PatentApplication Publication US 2002/0002327 A1 and U.S. Pat. No. 6,839,581describe methods for detecting Cheyne-Stokes respiration, which may beused on patients with heart failure. The methods involve performingspectral analysis of overnight oximetry recordings, from which aclassification tree is generated. Another method, based on monitoringoxygen saturation and calculating the slope of desaturation events, isdescribed in U.S. Pat. No. 6,760,608. Yet another method for classifyingsleep apneas is described in U.S. Pat. No. 6,856,829. In this case,pulse waves from the body of a patient are detected, and the envelope ofthe pulse waves is created by connecting every peak of the pulse waves.The normalized amplitude and period of the envelope are used indetermining whether the patient has OSA, CSA, or mixed sleep apneasyndrome. The disclosures of the patents and patent applications citedabove are incorporated herein by reference.

It has been suggested that sleep monitoring can be used for assessingcardiorespiratory risk. For example, U.S. Pat. No. 5,902,250, whosedisclosure is incorporated herein by reference, describes a home-based,wearable, self-contained system that determines sleep-state andrespiratory pattern, and assesses cardiorespiratory risk. A respiratorydisorder may be diagnosed from the frequency of eyelid movements and/orfrom ECG signals. Cardiac disorders (such as cardiac arrhythmia ormyocardial ischemia) that are known to be linked to certain respiratorydisorders also may be inferred upon detection of such respiratorydisorders.

Photoplethysmograph devices, known commonly as pulse oximeters, provideinstantaneous in vivo measurement of arterial oxygenation by determiningthe color of blood between a light source and a photodetector. Todetermine the blood oxygen saturation, light absorption measurement iscarried out at two wavelengths in the red and infrared ranges. Thedifference between background absorption during diastole and peakabsorption during systole at both wavelengths is used to compute theblood oxygen saturation.

Photoplethysmograph signals provide information not only on bloodoxygenation, but also on other physiological signs. For example, U.S.Pat. No. 5,588,425 describes the use of a pulse oximeter in validatingthe heart rate and/or R-R intervals of an ECG, and for discriminatingbetween sleep and wakefulness in a monitored subject. It also describesa method for distinguishing between valid pulse waveforms in theoximeter signal. U.S. Pat. No. 7,001,337 describes a method forobtaining physiological parameter information related to respirationrate, heart rate, heart rate variability, blood volume variabilityand/or the autonomic nervous system using photoplethysmography. U.S.Pat. No. 7,190,261 describes an arrhythmia alarm processor, whichdetects short-duration, intermittent oxygen desaturations of a patientusing a pulse oximeter as a sign of irregular heartbeat. An alarm istriggered when the pattern of desaturations matches a reference pattern.The disclosures of the above-mentioned patents are incorporated hereinby reference.

SUMMARY OF THE INVENTION

The photoplethysmograph signals that are output by a standard pulseoximeter can provide a wealth of information regarding the patient'svital signs and physiological condition. In embodiments of the presentinvention that are described hereinbelow, photoplethysmograph signalsthat are captured while the patient sleeps are analyzed in order todiagnose the patient's cardiorespiratory condition. In particular, thesignals may be used to detect and assess the severity of conditions thatare characteristic of heart failure (HF), such as premature ventricularcontractions and Cheyne-Stokes breathing. The photoplethysmographsignals may also be used, even without monitoring other physiologicalparameters, to classify the sleep stages and “sleep quality” of thepatient.

The power and versatility of the photoplethysmograph-based techniquesthat are described hereinbelow make it possible to monitor patients'oxygen saturation, heartbeat, respiration, sleep stages and autonomicnervous system during sleep using no more than a single pulse oximeterprobe (which typically clips onto the patient's finger). As a result,the patient may be monitored comfortably and conveniently, at home or ina hospital bed, even without on-site assistance in setting up eachnight's monitoring.

In alternative embodiments, the principles of the present invention maybe applied to analysis of respiration signals captured using monitors ofother types.

There is therefore provided, in accordance with an embodiment of thepresent invention, a method for diagnosis, including:

receiving from a sensor coupled to a body of a sleeping patient aphotoplethysmograph signal;

processing the photoplethysmograph signal independently of any otherphysiological measurements in order to identify sleep stages of thepatient.

In some embodiments, the method includes processing thephotoplethysmograph signal in order to measure a vasomodulation in thebody, to measure a heart rate of the patient, or to detect an artifactthat is characteristic of motion of the patient.

There is also provided, in accordance with an embodiment of the presentinvention, a method for diagnosis, including:

receiving a signal associated with blood oxygen saturation of a patientduring sleep;

processing the signal to detect occurrences of a pattern ofCheyne-Stokes breathing;

processing the signal to identify sleep stages of the patient; and

analyzing occurrences of the pattern relative to the identified sleepstages so as to determine a distribution of the Cheyne-Stokes breathingper sleep stage.

There is additionally provided, in accordance with an embodiment of thepresent invention, apparatus for diagnosis, including:

a sensor, which is configured to be coupled to a body of a sleepingpatient and to output a photoplethysmograph signal; and

a processor, which is coupled to process the photoplethysmograph signalindependently of any other physiological measurements in order toidentify sleep stages of the patient.

There is further provided, in accordance with an embodiment of thepresent invention, apparatus for diagnosis, including:

a sensor, which is configured to output a signal associated with bloodoxygen saturation of a patient during sleep; and

a processor, which is coupled to process the signal to detectoccurrences of a pattern of Cheyne-Stokes breathing and to identifysleep stages of the patient, and to analyze occurrences of the patternrelative to the identified sleep stages so as to determine adistribution of the Cheyne-Stokes breathing per sleep stage.

There is moreover provided, in accordance with an embodiment of thepresent invention, a computer software product, including acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a computer, cause the computer to receive aphotoplethysmograph signal from a body of a sleeping patient, and toprocess the photoplethysmograph signal independently of any otherphysiological measurements in order to identify sleep stages of thepatient.

There is furthermore provided, in accordance with an embodiment of thepresent invention, a computer software product, including acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a computer, cause the computer to receive asignal associated with blood oxygen saturation of a patient duringsleep, and to process the signal to detect occurrences of a pattern ofCheyne-Stokes breathing and to identify sleep stages of the patient, andto analyze occurrences of the pattern relative to the identified sleepstages so as to determine a distribution of the Cheyne-Stokes breathingper sleep stage.

There is also provided, in accordance with an embodiment of the presentinvention, apparatus for monitoring a patient, including:

a plethysmographic sensor, which is configured to fit over a finger ofthe patient and to output a signal associated with blood oxygensaturation of the patient; and

a control unit, which is configured to be fastened to a forearm of thepatient and is coupled to receive the signal from the plethysmographicsensor, and which includes:

a memory;

a signal processor, which is coupled to digitize and process the signalso as to generate data indicative of a pattern of breathing by thepatient and to store the data in the memory;

an interface, which is configured to be coupled to an external processorfor upload of the data from the memory to the external processor; and

a power source, which is coupled to provide electrical power to thesignal processor and the sensor so as to enable collection of the datawhile the interface is disconnected from the external processor.

In a disclosed embodiment, the plethysmographic sensor is configured asa ring, which fits over the finger, and the control unit is configuredto be fastened around a wrist of the patient. The control unit mayinclude an actigraph sensor, for sensing movement of the patient.

Typically, the power source is rechargeable, and the interface includesa charging circuit for recharging the power source using electricalpower received via the interface while the interface is coupled to theexternal processor.

In one embodiment, the signal processor is configured to process thesignal so as to detect episodes of periodic breathing.

The present invention will be more fully understood from the followingdetailed description of the embodiments thereof, taken together with thedrawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic, pictorial illustration of a system for sleepmonitoring and diagnosis, in accordance with an embodiment of thepresent invention;

FIG. 2A is a schematic, pictorial illustration of apparatus for patientmonitoring, in accordance with an embodiment of the present invention;

FIG. 2B is a block diagram that schematically shows functional elementsof the apparatus of FIG. 2A, in accordance with an embodiment of thepresent invention;

FIGS. 3A and 3B are a flow chart that schematically illustrates a methodfor sleep monitoring and diagnosis, in accordance with an embodiment ofthe present invention;

FIG. 4 is a schematic plot of photoplethysmograph and ECG signals,illustrating detection of a cardiac arrhythmia in accordance with anembodiment of the present invention;

FIG. 5 is a Kaplan-Meier plot that schematically shows survival of heartfailure patients as a function of Brain Natriuretic Peptide (BNP)levels;

FIG. 6 is a Kaplan-Meier plot that schematically shows survival of heartfailure patients as a function of BNP levels, with a threshold leveldetermined by severity of Cheyne-Stokes breathing, in accordance with anembodiment of the present invention;

FIG. 7 is a receiver operating characteristic (ROC) plot, whichschematically shows the sensitivity and specificity of predicting heartfailure prognosis in accordance with an embodiment of the presentinvention; and

FIG. 8 is a Kaplan-Meier plot that schematically shows survival of heartfailure patients as a function of the severity of symptoms classified bya method in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS System Overview

FIG. 1 is a schematic, pictorial illustration of a system 20 for sleepmonitoring and diagnosis, in accordance with an embodiment of thepresent invention. In this embodiment, system 20 is used to monitor apatient 22 in a home, clinic or hospital ward environment, although theprinciples of the present invention may similarly be applied indedicated sleep laboratories. System 20 receives and analyzes aphotoplethysmograph signal from a suitable sensor, such as a pulseoximetry device 24. Device 24 provides a photoplethysmograph signalindicative of blood flow and a signal indicative of the level of oxygensaturation in the patient's blood. In the context of the present patentapplication and in the claims, the photoplethysmograph signal is thusconsidered to be a signal that is associated with blood oxygensaturation. Since the photoplethysmograph signal is modulated by boththe heart rate and respiratory rate, it may also be used to provide aheart rate and respiratory rate signals. The sensor signals from device24 are collected, digitized and processed by a console 28.

Optionally, system 20 may comprise sensors of other types (not shown),for collecting other physiological signals. For example, the system mayreceive an ECG signal, measured by skin electrodes, and a respirationsignal measured by a respiration sensor. Additionally or alternatively,the techniques of monitoring and analysis that are described herein maybe combined with EEG, EOG, leg motion sensors, and other sleep and/orcardiac monitoring modalities that are known in the art. As anotherexample, console 28 may receive signals by telemetry from implantablecardiac devices, such as pacemakers and ICDs.

Console 28 may process and analyze the signals from pulse oximetrydevice 24 locally, using the methods described hereinbelow. In thepresent embodiment, however, console 28 is coupled to communicate over anetwork 30, such as a telephone network or the Internet, with adiagnostic processor 32. This configuration permits sleep studies to beperformed simultaneously in multiple different locations. Processor 32typically comprises a general-purpose computer processor (which may beembedded in a bedside or remote monitor) with suitable software forcarrying out the functions described herein. This software may bedownloaded to processor 32 in electronic form, or it may alternativelybe provided on tangible media, such as optical, magnetic or non-volatileelectronic memory. Processor 32 analyzes the signals conveyed by console28 in order to analyze the physiological parameters, identify sleepstages, and extract prognostic information regarding patient 22, and todisplay the results of the analysis to an operator 34, such as aphysician.

Alternatively, although the embodiments described herein relate mainlyto methods and apparatus for monitoring and diagnosis during sleep, theprinciples of the present invention may also be applied, mutatismutandis, to patients who are awake. In particular, these methods andapparatus may be used in monitoring patients who are reclining orotherwise at rest, even if they are not asleep.

Reference is now made to FIGS. 2A and 2B, which schematically illustrateapparatus 21 for patient monitoring, in accordance with an embodiment ofthe present invention. FIG. 2A is a pictorial illustration of theapparatus, while FIG. 2B is a block diagram showing functionalcomponents of the apparatus. Apparatus 21 is similar in functionality toelements of system 20, as shown in FIG. 1, but apparatus 21 isparticularly advantageous in that it can be worn comfortably by thepatient during both sleep and waking hours and requires no wiredconnection to a console, except periodically (once a day, for example)for data upload and battery recharging.

As shown in FIG. 2A, pulse oximetry device 24 in apparatus 21 has theform of a ring, which fits comfortably over one of the fingers on a hand23 of the patient (although other configurations of device 24 mayalternatively be used in the apparatus). Device 24 is connected by awire to a control unit 25, which may conveniently be fastened around thepatient's wrist. Alternatively, the control unit may be fastenedelsewhere on the patient's forearm, at any suitable location between thehand and the elbow, or elsewhere on the patient's body. The control unitmay include a display 27, to present status information and/or readingsof monitored parameters, such as heart rate, blood oxygen saturation andheart failure status. A connector 29 on the control unit is configuredto connect to a console or docking station. In the illustratedembodiment, connector 29 comprises a receptacle for a cable with astandard plug, such as a USB cable. Alternatively, the connector maymate directly with a matching connector on a dedicated docking station.

As shown in FIG. 2B, a sensor 31 (typically comprising two lightsource/light detector subassemblies, as described below) in device 24 isconnected via wire to signal processing circuitry 33 in control unit 25.The signal processing circuitry digitizes and filters the signals fromsensor 31 and stores the results in a memory 35. (Alternatively oradditionally, control unit 25 may transmit the results to a receiverusing a suitable wireless communication protocol, such as Bluetooth® orZigBee®). Optionally, circuitry 33 may also be configured to performsome of the additional processing functions that are shown in FIGS. 3Aand 3B and described hereinbelow. The signal processing circuitry andperipheral components are powered by an internal power source, such as abattery 38, so that apparatus 21 can perform its data collectionfunctions without wired connection to a console or to lines power.

Apparatus 21 may also comprise an actigraph 39, which is typicallycontained in control unit 25. The actigraph measures movement of thepatient and typically comprises an accelerometer for this purpose. Themeasurements of patient movement are recorded together with the datafrom sensor 31 in memory 35 and may be used in subsequent analysis todetermine the patient's state of sleep or arousal.

After apparatus 21 has recorded patient data in memory 35 for asufficient period of time, the user (who may be the patient himself orherself) connects control unit 25 to the docking station or otherconsole via connector 29. A controller 36 in the control unit is thenable to communicate with the console or docking station via a suitableinterface 37 (such as a USB interface in the example noted above). Thecontroller reads out the data that are stored in memory 35 to aprocessor, such as processor 32, which analyzes the data, as describedhereinbelow. In addition, interface 37 may comprise charging circuitryfor recharging battery 38.

In the embodiments that are described below, pulse oximetry device 24may be configured either as shown in FIG. 1 or as shown in FIG. 2A or2B. Alternatively, substantially any suitable sort ofphotoplethysmograph may be used in these embodiments, including aphotoplethysmographic sensor that is implanted in the body of thepatient. An implantable oximeter that may be used for this purpose, forexample, is described in U.S. Pat. No. 6,122,536, whose disclosure isincorporated herein by reference. Furthermore, the methods that aredescribed hereinbelow may be used in conjunction with devices of othertypes that provide information on the breathing, oxygen saturation, andheart performances of the patient. In one embodiment, for instance, themethods described below are applied to the output of a non-contactrespiratory monitor, such as the one described in U.S. Pat. No.6,011,477, whose disclosure is incorporated herein by reference.

Diagnostic Method

FIGS. 3A and 3B are a flow chart that schematically illustrates a methodfor sleep monitoring and diagnosis, in accordance with an embodiment ofthe present invention. Pulse oximetry device 24 comprises two lightsource/light detector subassemblies 40 and 42. These subassembliesgenerate signals that are indicative of absorption and/or reflectance oflight at two different wavelengths, typically one red and one infrared,as is known in the art. Each signal includes an AC component, whichcorresponds to the pulsatile change in the signal at the patient's heartrate, and a slow-changing DC component. Comparison of the AC componentsof the two signals gives a blood oxygen saturation signal 44.Alternatively, at least some of the methods described below can use thesignals from only a single source/detector subassembly, or signalsprovided by other types of photoplethysmographic sensors.

The saturation signal is low-pass filtered to give a very-low-frequency(VLF) saturation signal 46. This filtering removes signal components atfrequencies that are greater than or equal to the patient's respiratoryfrequency, so that the signal remaining reflects trends over multiplerespiratory cycles. In some embodiments, the filtering is even morepronounced, and eliminates frequency components outside theCheyne-Stokes cycle frequency, for example, components below 1/180 Hz orabove 1/40 Hz.

Processor 32 analyzes shape characteristics of the VLF saturation signalin order to detect episodes of Cheyne-Stokes breathing (CSB). As notedearlier, this condition is characterized by a regular waxing and waningbreathing pattern and occurs particularly among patients with heartfailure and in patients who have experienced a stroke. CSB is presentduring sleep, and in more severe cases may also be observed duringwakefulness. According to the American Academy of Sleep Medicine,Cheyne-Strokes breathing syndrome (CSBS) is characterized by thefollowing criteria:

-   -   1. Presence of congestive heart failure or cerebral neurological        disease.    -   2. Respiratory monitoring demonstrates:        -   a. At least three consecutive cycles of a cyclical crescendo            and decrescendo change in breathing amplitude. Cycle length            is most commonly in the range of 60 seconds, although the            length may vary.        -   b. One or both of the following:            -   i. Five or more central sleep apneas or hypopneas per                hour of sleep.            -   ii. The cyclic crescendo and decrescendo change in                breathing amplitude has duration of at least 10                consecutive minutes.

The inventors have found the typical Cheyne-Stokes cycle length to bebetween 40 and 90 sec. The decrescendo phase is associated withdecreased respiratory effort and rate (hypopnea/apnea); decrease inoxygen saturation; decrease in heart rate; and vasodilation, manifestedin decreased blood pressure. The crescendo phase has the oppositeeffects: increase in respiratory effort and rate, i.e. hyperpnea;increase in heart rate; and vasoconstriction, leading to increased bloodpressure. Sometimes the hyperpnea is accompanied by an arousal, which ismanifested as a motion artifact in the photoplethysmograph signal. Thechanges in heart rate and vasomotion (dilation and constriction) dependon the severity of the heart failure, as discussed below.

The inventors have also found that decompensated heart failure patientsnearly always present long sequences of periodic Cheyne-Stokes breathingepisodes, with a cycle length between 55 and 180 seconds. In general,the longer the cycle length, the more severe is the state of thedisease. Therefore, processor 32 uses the shape characteristics of theVLF saturation signal in measuring time characteristics 48 of thepatient's Cheyne-Stokes episodes. Specifically, the processor detectsdesaturation episodes extending over multiple consecutive Cheyne-Stokesbreathing cycles in order to identify the presence of CSBS.

In order to detect and measure the duration of multi-cycle Cheyne-Stokesepisodes, processor 32 typically locates the local maxima and localminima of the VLF saturation signal. The processor may also compute thedifference between the maximal and minimal saturation values (in theunsmoothed saturation signal 44), as well as the correspondingwavelengths. The processor extracts time sequences of cyclic breathingwith similar desaturation values and similar wavelengths, falling in therange that is characteristic of Cheyne-Stokes cycles. (Typically only acertain percentage, such as 80%, of the desaturation and wavelengthvalues are required to be close to the median values of the sequence, inorder to avoid losing sequences due to intervening outliers. Forexample, a 50% deviation from the median value of 80% of the wavelengthand desaturation values may be accepted for a sequence that is at leastof a certain minimum duration, such as 5 min.) The processor chooses thelongest segments that meet the above similarity criteria. Alternatively,a hysteresis procedure may be used to ensure robustness againstoutliers. The total Cheyne-Stokes time is then computed as the totalduration of all the segments that are classified as Cheyne-Stokesbreathing events.

In order to validate the automatic measurements of Cheyne-Stokesepisodes described above, the inventors conducted a clinical trial,which included 91 full-night ambulatory polysomnography tests forpatients with advanced heart failure. Cheyne-Stokes episodes were markedmanually by an experienced scorer, and these manual results werecompared to the results of the automatic process described above. Thecorrelation between manual automatic scoring was 83%, which is as goodas the typical correlation between different human scorers.

To ensure further that the sequences of cyclic breathing episodes areindeed associated with the severity of heart failure status, processor32 evaluates the slope of the saturation signal (or of the DC componentof the pulse oximeter signal) for each desaturation event. In centralapnea, or when the heart failure state is grave, the slope of the exitfrom the cycle is moderate, i.e., it is similar to the typical (orspecific) entry slope. Therefore, to identify a time sequence of cyclicbreathing as Cheyne-Stokes, processor 32 requires that the sequencecomprise mainly (typically at least 80%) events of moderate slope. (Theabove-mentioned PCT Patent Application PCT/IL2006/000148 defines formalcriteria for assessing the symmetry of periodic breathing episodes,which may also be used in the present context for distinguishingCheyne-Stokes events.) This requirement of moderate slope may be appliedto the median slope value.

These observations with respect to the symmetry of the periodicbreathing patterns apply both to the slowly-varying heart rate andsaturation signals and to the envelopes of the other, rapidly-varyingsignals shown by the other traces. The term “envelope” in this contexttypically means a signal derived from the local minima and/or localmaxima of another signal, with or without smoothing (by convolution orresampling, for example). “Envelopes” may also be derived by othermathematical operations known in the art, such as application of Hilberttransforms. The inventors have found that periodic breathing patternsassociated with CSA generally tend to be more symmetrical than thepatterns associated with OSA, presumably due to the differentphysiological mechanisms that are involved in the different types ofapneas. Therefore, processor 32 may validate the prognostic value of theCheyne-Stokes marker by considering only events with mild exit slopefrom desaturation events. The inventors found that computing the slopeof the saturation curve by fitting a line (by the least-square method)to the curve over a nine-second epoch, and requiring that the slope ofthe line be less then 0.7 percent/second is a good implementation ofthis mild desaturation condition.

Processor 32 associates each segment with its segment duration and withits median desaturation value. The features of the Cheyne-Stokessegments are prognostic of patient outcome in cases of heart failure(and other illnesses). Long wavelength, in particular, is associatedwith bad prognosis. Thus, processor 32 typically detects signalcomponents that have a period greater than a minimum period of at least30 sec. In the marker validation experiments that are described herein,the inventors required the median cycle length to be above 55 secondsand the median desaturation value to be no less the 2% in order toclassify a periodic breathing pattern as Cheyne-Stokes breathing.

On the other hand, time segments with steep exit saturation slopetypically correspond to obstructive apnea/hypopnea events. Otherfeatures of obstructive apnea/hypopnea time segments include shortwavelength, large vasomotion, and large heart rate modulations. Thesephenomena are generally associated with good prognosis, since theyreflect the patient's ability to manifest enhanced sympathetic activity.

In addition to the saturation measurements and Cheyne-Stokes detection,processor 32 may also process an AC absorption or reflectance signal 52that is output by device 24 in order to compute a heart rate 54, as isknown in the art. Furthermore, the AC signal may be analyzed to detect abeat morphology 56. The processor identifies certain aberrations in thismorphology as arrhythmias, such as premature ventricular contractions(PVCs) 58. It keeps a record of the occurrences of such arrhythmias, ina manner similar to a Holter monitor, but without requiring the use ofECG leads. The total number of abnormal heart beats and (specificallyPVCs) that are accumulated in such a record, particularly during sleep,is indicative of bad prognosis. As the inventors have found thatpremature beats during sleep have the greatest prognostic value foradvanced heart failure patients, the processor may be configured tocount the number of premature beats only during sleep or during episodesof Cheyne-Stokes breathing.

FIG. 4 is a schematic plot of an AC photoplethysmograph signal 60,alongside a corresponding ECG signal 62, illustrating a method fordetection of PVCs in accordance with an embodiment of the presentinvention. Signal 60 can be seen to comprise a series of regularwaveforms, which are indicative of arterial blood flow. A PVC ismanifested as an aberrant waveform 64 in signal 60, and likewise by anabnormal waveform 66 in signal 62. Processor 32 analyzes the shape,amplitude and timing of waveform 64 in the plethysmograph signal inorder to determine that the aberrant wave represents PVC, even withoutthe use of any sort of ECG monitoring.

In one embodiment, arrhythmias are identified in photoplethysmographsignal 60 based on the following features:

-   -   1. Local maxima and minima are extracted from the signal in        segments of the signal whose length is less than the typical RR        interval (i.e., the typical time difference between successive        heart beats). For example, 0.3 seconds is an appropriate segment        length for this purpose.    -   2. The width of each beat is defined, for example by measuring        the time difference between successive locations of        photoplethysmograph signal values whose energy is equal to the        average (possibly a weighted average) of the local maximum and        minimum.    -   3. Beats with short width typically correspond to PVCs, as shown        in FIG. 4. The number of such beats is a measure of the severity        of arrhythmia.    -   4. An additional criterion for detecting an arrhythmia is that        the time span of two beats, one of which has a short width, is        roughly equal to the time span of two normal beats.

Although FIG. 4 and the above description relate specifically to PVCs,the principles of this embodiment may likewise be applied in detectingother types of premature heart beats, as well as various other types ofheartbeat irregularities. Such irregularities are associated withreduced stroke volume, which in turn affect the amplitude, width andother features of the photoplethysmograph waveform.

Other types of aberrant waveforms in photoplethysmograph signal 60 maycorrespond to motion artifacts 80 (listed in FIG. 3A, but not shown inFIG. 4). Motion is characterized by local maxima well above normal beatrange (for example, at least twice the normal value). The prevalence ofmotion artifacts can be used in detecting movement, which indicatewhether the patient is in a sleep or waking state 82. Alternatively oradditionally, a motion sensor may be used to detect arousals.

Referring further to FIGS. 3A and 3B, processor 32 may additionallyextract other cardiorespiratory parameters from signal 52, eitherdirectly or indirectly. For example, the processor may applyvery-low-frequency filtering to heart rate 54 in order to detect heartrate modulations 70. Additionally or alternatively, the envelope ofsignal 52 may be processed in order to detect characteristics ofvasomodulation 72, i.e., arterial dilation and constriction.

Further additionally or alternatively, processor 32 may compute arespiration energy and/or rate characteristic 74 based on high-frequencycomponents of signal 52. Respiratory sinus arrhythmia is a natural cycleof arrhythmia that occurs in healthy people through the influence ofbreathing on the flow of sympathetic and vagus impulses to thesinoatrial node in the heart. This effect may be used to calculaterespiration from heart rate. Well-treated heart failure patients,however, are frequently under the control of cardiac pacemakers andoften take beta-blockers and ACE inhibitors that suppress thisphenomenon. High-frequency (10-30 cycles/min, i.e., 0.17-0.5 Hz)filtering of the photoplethysmograph signal enables the processor todetermine respiration energy and/or rate characteristics in these cases,as well.

Very-low-frequency components of characteristic 74 are indicative of arespiration modulation 76. Processor 32 combines the various cardiac,respiratory and vasomodulation parameters described above in order toprovide a general picture of cardiorespiratory effects 78, all on thebasis of the photoplethysmograph signals.

Similar procedures to those described above can be applied to thedetrended AC photoplethysmograph signal. One way to perform detrendingis to replace the photoplethysmograph signal with its amplitude feature(maximum minus minimum signal). Other methods include subtracting apolynomial that approximates the signal, or using local maxima or localminima features. Following detrending, the processor appliesvery-low-frequency filtering followed by outlier rejection, and thencomputes the median vasomotion of each sequence.

The processor may perform similar analyses on heart rate and respiratorysignals from other sources. Arousals can estimated from motion artifactsas described above or from other data if available (such as EEG alphaand beta frequencies, or scorer marking, or a motion sensor).

Information regarding sleep/wake state 82 is combined with Cheyne-Stokestime 48 to determine specific, cumulative Cheyne-Stokes time 84 duringsleep. The total Cheyne-Stokes time and percentage of Cheyne-Stokes timeduring sleep have prognostic value: A large percentage of Cheyne-Stokestime is associated with mortality and high levels of brain natriureticpeptide (BNP), which are associated with severity of heart failure.Furthermore, information about sleep time can be used to ensure that lowCheyne-Stokes duration is not associated with little or no sleep. (Theinventors have determined the prognostic value of total Cheyne-Stokestime only in patients who slept for at least a certain minimal duration,such as two hours.) The prognostic value of Cheyne-Stokes informationderived in the above manner is illustrated in FIGS. 5-8 below.

This information regarding Cheyne-Stokes time 84 in turn is combinedwith the general picture of cardiorespiratory effects 78 in order toprovide some or all of the following combined information 86 for eachCheyne-Stokes sequence during sleep:

-   -   1. duration    -   2. median wavelength    -   3. median desaturation    -   4. median vasomotion    -   5. median heart rate modulation    -   6. median respiratory modulation    -   7. number of PVCs and other premature beats.    -   8. arousal index: number of arousals        Alternatively, the above-mentioned median functions may be        replaced by similar functions based on average values or average        of values in the middle tertile, inter-quartile range, or any        other appropriate segment. Each of the above parameters can also        be computed separately for REM sleep and NREM sleep.

In an exemplary embodiment, the following criteria may be applied to thevarious processed outputs of oximetry device 24 in order to deriveinformation 86 and measure the manifestations of Cheyne-Stokesbreathing:

-   -   1. The saturation signal is filtered in the Cheyne-Stokes        frequency range, typically 1/180- 1/40 Hz.    -   2. A time segment is identified as a Cheyne-Stokes event (and        the durations of such time segments are summed) if the segment        contains a sequence of at least three cycles of desaturation for        which:        -   a. Median desaturation (compared to the previous saturation            level) is at least 2%. Alternatively, another representative            saturation level, such as the mean or minimum, may be used.        -   b. Mean cycle length is long (55-180 sec). (Short cycle            length is not associated with bad prognosis.)        -   c. Cycle length fluctuation within each sequence may            optionally be limited (to less then 10% fluctuation, for            example).        -   d. Moderate vasomotion, based on at least one of the            following:            -   i. Median of maximal desaturation slope in each cycle is                less then a maximum slope limit, such as 0.7                percent/sec. (by least squares fit of a line to the                desaturation curve). Alternatively, a measure of mean                slope may be used.            -   ii. Only moderate fluctuations (typically no greater                than 10% of the normal range) occur in the VLF range of                the detrended respiration signal.    -   3. Optionally, for a periodic breathing cycle to be identified        as part of a Cheyne-Stokes event, respiration characteristic 74        may be required to reach a minimum indicating zero respiratory        effort during the cycle. This minimum may be identified based on        the VLF components of characteristic 74 in respiration        modulation 76.    -   4. Outlier rejection procedures may be applied to the saturation        and respiration values before classifying time segments. For        example:        -   a. As noted above, a certain fraction (typically up to 20%)            of the desaturation and wavelength values may be far from            the median values of the sequence, and extreme desaturations            (for example, >50%) may be rejected as faulty readings.        -   b. The mean cycle length can be calculated after discarding            values that are far from the mean (for example, values in            the top and bottom deciles.)        -   c. Consistency may be enforced by permitting the relation            between cycle length and desaturation to vary linearly            within given bounds.    -   5. In addition to or instead of the above criteria,        self-similarity measures can be used in identifying sequences of        Cheyne-Stokes cycles. For example, a distinct peak in the 1/180-        1/40 Hz range of the Fourier transform of the sequence of        periodic breathing cycles or high autocorrelation of the cycles        is an indicator of such self-similarity.

Similarly, when a respiration signal is obtained without a saturationsignal, Cheyne-Stokes respiration segments can be found by applying theabove criteria to the respiration signal (excluding the computationsthat relate to saturation values).

Referring again to FIG. 3B, sleep/wake state 82 may be combined withanalysis of cardiorespiratory and Cheyne-Stokes effects in order toperform automatic sleep staging 88. All of the factors that are used indetermining the sleep stage may be derived solely from the signalsgenerated by oximetry device 24. Alternatively, other signals may beincorporated into the sleep staging calculation.

Sleep states are classified by processor 32 as light sleep, deepslow-wave sleep (SWS) 90 and REM 92. During REM sleep, the patient ispartially paralyzed, so that there is no motion. Furthermore, due to thechanges in autonomic control and the partial paralysis that characterizeREM sleep, the Cheyne-Stokes wavelength tends to be longer and thedesaturation deeper in REM sleep that in light sleep. On the other hand,there are no apnea episodes in deep sleep. Others factors characterizingdeep sleep include regularity of respiratory cycle length and lowvasomotion.

Processor 32 may use the distribution of sleep stages and of apneaevents during sleep in computing a sleep quality index 94. Typically,high percentages of REM and SWS, as well as apnea-free (or nearlyapnea-free) segments in non-SWS sleep, are indicative of good prognosisfor heart failure patients. By contrast, low percentages of REM or SWSindicate a poor prognosis. Further aspects of sleep staging and sleepquality assessment are described in the above-mentioned PCT patentapplications.

Clinical Application and Results

The methods described above for measuring and quantifying Cheyne-Stokesbreathing and attendant heart failure prognosis may be used convenientlyin performing frequent checks on patient status, both at home and in thehospital. Additionally or alternatively, occasional checks of this sortmay be used for risk stratification and screening. As explained above,these methods may be implemented using measurements made solely by pulseoximetry device 24, or alternatively in conjunction with other sensors,as in a multi-monitor polysomnography system, or in an implantabledevice, or using other types of respiratory sensors.

The inventors compared Cheyne-Stokes time with heart failure status in91 tests of advanced heart failure patients. Results of this study arepresented below. The cumulative duration of Cheyne-Stokes breathingduring a night's sleep was measured, wherein Cheyne-Stokes cycles wereidentified as described above (including the requirements of mildslope—up to 0.7 percent/sec, median desaturation of at least 2%, andmedian cycle length of 55 to 180 sec.) The status of the patients wasdetermined by six-month survival and BNP levels, which are generallyconsidered the best marker for heart failure status. For this purpose, ablood sample was drawn from each patient and tested for NT-proBNP on thenight of the sleep study. Serum N-terminal prohormone Brain NatriureticPeptide (NT-proBNP) was measured using the Elecsys® proBNPelectro-chemiluminescence immunoassay, run on the Elecsys 1010 benchtopanalyzer (Roche Diagnostics, Indianapolis, Ind.).

FIG. 5 is a Kaplan-Meier plot of patient survival according to thestandard BNP kit values. According to accepted diagnostic standards, astate of decompensated heart failure is associated with a serumNT-proBNP level above 450, 900, or 1,800 pg/mL for patients whose age isless than 50, 50-75, or above 75, respectively. An upper trace 70 showsthe rate of survival over time for the patients with low BNP (below thedecompensation threshold), while an upper trace 72 shows the rate forpatients with high BNP. The odd ratio for dying among patients with high(decompensated) BNP values was 3.5 times higher then for patients in thelow-BNP group. This result, however, was not statistically significantin this trial (p=0.18).

FIG. 6 is a Kaplan-Meier plot of patient survival according to theautomated Cheyne-Stokes marker described above, in accordance with anembodiment of the present invention. In this figure, an upper trace 74shows the survival rate of the patients who had low cumulative durationof Cheyne-Stokes breathing episodes, while a lower trace 76 shows thesurvival rate for patients with high cumulative Cheyne-Stokes duration.The inventors have found that typically, a cumulative duration ofCheyne-Stokes breathing episodes in excess of 45 minutes during anight's sleep is indicative of poor prognosis. In the results shown inFIG. 6, the Cheyne-Stokes cutoff (48 minutes) was selected to bestpredict BNP cutoff according to the standard guidelines described above.

Comparison of FIGS. 5 and 6 shows that the respiration-based measure ofheart failure severity is superior to the standard BNP test inpredicting mortality: The odd ratio for dying among patients with highCheyne-Stokes duration is better then 5.2. (There was no mortality atall in the low Cheyne-Stokes group.) The results are statisticallyhighly significant (p=0.017). For some higher cutoff points ofCheyne-Stokes time, the odd ratio improved even further. Furthermore,these results also demonstrate that Cheyne-Stokes duration is a strongpredictor of BNP level and differentiates between compensated anddecompensated levels of heart failure.

FIG. 7 is a receiver operating characteristic (ROC) plot, whichschematically compares the sensitivity and specificity of predictingheart failure prognosis using BNP and duration of Cheyne-Stokesbreathing episodes, as measured in accordance with an embodiment of thepresent invention. An upper trace 78 is the ROC curve for Cheyne-Stokesduration, while a lower trace 80 is the ROC curve for the BNP marker. Toderive the results shown in the figure, both Cheyne-Stokes duration andBNP were tested against six-month mortality of the patients in thestudy. The plot shows that the Cheyne-Stokes marker gives greatersensitivity and specificity. The area under the curve (AUC) for BNP is68% (p=0.082), while it is 75% (p=0.015) for the Cheyne-Stokes marker.

FIG. 8 is a Kaplan-Meier plot of six-month survival of the heart failurepatients as a function of the severity of symptoms classified by themethods described above, in accordance with another embodiment of thepresent invention. In this case, patients were classified into twogroups: one group with severe Cheyne-Stokes breathing coupled withcardiac arrhythmia, and the other with breathing and heart rhythm thatshowed mild or no symptoms of these kinds. For this purpose, patientswho exhibited at least 200 premature beats in the course of the night'ssleep were classified as suffering from cardiac arrhythmia. An uppertrace 82 in the figure shows the survival rate for the group with mildor no symptoms, while a lower trace 84 shows the survival rate forpatients in the severe/arrhythmic group. The plot demonstrates that thecombined detection of Cheyne-Stokes breathing and cardiac arrhythmias isan even stronger predictor of survival than Cheyne-Stokes breathing byitself: The odd ratio in this case was 17.7 with p=0.000.

The results of FIGS. 5-8 show that photoplethysmographic monitoringduring sleep may be used effectively for the prognosis of heart failurepatients. As explained above, this sort of monitoring is simple to carryout in the patient's home or hospital bed and may be performed atregular intervals, at low cost and minimal discomfort to the patient. Itprovides physicians with an accurate prognostic indicator, which theycan use in choosing the optimal treatment, such as determining whether apatient requires hospitalization, and adjusting treatment parameters(such as drug titration) based on changes in the patient's condition.For example, using the techniques described above, the physician maymeasure and quantify the patient's symptoms (Cheyne-Stokes duration andpossibly arrhythmias) prior to initiating or making a change intreatment, and may repeat the measurement thereafter in order to assessthe effectiveness of the treatment and possibly readjust treatmentparameters.

As another alternative, the physician may fix a specific criticalCheyne-Stokes duration for individual patients, and then set amonitoring system to alarm whenever a specific duration is exceeded.

Although the embodiments described above relate mainly to signalscaptured by pulse oximetry device 24, the principles of the presentinvention may be applied to respiration signals captured by any othersuitable type of sensor. Such sensors may be based, for example, onelectrical measurements of thoracic and abdominal movement, using skinelectrodes to make a plethysmographic measurement of the patient'srespiratory effort, or a belt to sense changes in the body perimeter.Additionally or alternatively, air flow measurement, based on a pressurecannula, thermistor, or CO2 sensor, may be used for respiration sensing.In other embodiments of the present invention, a capnograph may be usedin detecting sleep apneas, either in conjunction with or separately fromthe pulse oximeter used in the techniques described above.

It will thus be appreciated that the embodiments described above arecited by way of example, and that the present invention is not limitedto what has been particularly shown and described hereinabove. Rather,the scope of the present invention includes both combinations andsubcombinations of the various features described hereinabove, as wellas variations and modifications thereof which would occur to personsskilled in the art upon reading the foregoing description and which arenot disclosed in the prior art.

1. A method for diagnosis, comprising: receiving from a sensor coupledto a body of a sleeping patient a photoplethysmograph signal; andoperating a data processor for processing the photoplethysmograph signalindependently of any other physiological measurements in order toidentify apnea episodes which comprise Cheyne-Stokes breathing eventsand to distinguish a light sleep stage from a deep sleep stage of thepatient based on the identified apnea episodes; wherein processing thephotoplethysmograph signal comprises distinguishing a rapid eye movement(REM) sleep stage from a light sleep stage responsively to acharacteristic of the Cheyne-Stokes breathing events, selected from thegroup of characteristics consisting of a Cheyne-Stokes wavelength and adesaturation depth.
 2. The method according to claim 1, and comprisingprocessing the photoplethysmograph signal in order to measure avasomodulation in the body.
 3. The method according to claim 1, andcomprising processing the photoplethysmograph signal in order to measurea heart rate of the patient.
 4. The method according to claim 1, andcomprising processing the photoplethysmograph signal in order to detectan artifact that is characteristic of motion of the patient. 5.Apparatus for diagnosis, comprising: a sensor, which is configured to becoupled to a body of a sleeping patient and to output aphotoplethysmograph signal; and a processor, which is coupled to processthe photoplethysmograph signal independently of any other physiologicalmeasurements in order to identify apnea episodes which compriseCheyne-Stokes breathing events and to distinguish a light sleep stagefrom a deep sleep stage of the patient based on the identified apneaepisodes; wherein the processor is configured to distinguish a rapid eyemovement (REM) sleep stage from a light sleep stage responsively to acharacteristic of the Cheyne-Stokes breathing events, selected from thegroup of characteristics consisting of a Cheyne-Stokes wavelength and adesaturation depth.
 6. The apparatus according to claim 5, wherein theprocessor is configured to process the photoplethysmograph signal inorder to measure a vasomodulation in the body.
 7. The apparatusaccording to claim 5, wherein the processor is configured to process thephotoplethysmograph signal in order to measure a heart rate of thepatient.
 8. The apparatus according to claim 5, wherein the processor isconfigured to process the photoplethysmograph signal in order to detectan artifact that is characteristic of motion of the patient.
 9. Acomputer software product, comprising a computer-readable medium inwhich program instructions are stored, which instructions, when read bya computer, cause the computer to receive a photoplethysmograph signalfrom a body of a sleeping patient, and to process thephotoplethysmograph signal independently of any other physiologicalmeasurements in order to identify apnea episodes which compriseCheyne-Stokes breathing events, to distinguish a light sleep stage froma deep sleep stage of the patient based on the identified apneaepisodes, and to distinguish a rapid eye movement (REM) sleep stage froma light sleep stage responsively to a characteristic of theCheyne-Stokes breathing events, selected from the group ofcharacteristics consisting of a Cheyne-Stokes wavelength and adesaturation depth.
 10. The product according to claim 9, wherein theinstructions cause the computer to process the photoplethysmographsignal in order to measure a vasomodulation in the body.
 11. The productaccording to claim 9, wherein the instructions cause the computer toprocess the photoplethysmograph signal in order to measure a heart rateof the patient.
 12. The product according to claim 9, wherein theinstructions cause the computer to process the photoplethysmographsignal in order to detect an artifact that is characteristic of motionof the patient.
 13. A method for diagnosis, comprising: receiving from asensor coupled to a body of a sleeping patient a photoplethysmographsignal; and operating a data processor for processing thephotoplethysmograph signal in order to identify apnea episodes whichcomprise Cheyne-Stokes breathing events and to distinguish a light sleepstage from a deep sleep stage of the patient based on the identifiedapnea episodes; wherein said processing comprises distinguishing a rapideye movement (REM) sleep stage from a light sleep stage responsively toa characteristic of the Cheyne-Stokes breathing events, selected fromthe group of characteristics consisting of a Cheyne-Stokes wavelengthand a desaturation depth.
 14. The method according to claim 13, andcomprising processing the photoplethysmograph signal in order to measureat least one of a vasomodulation in the body and a heart rate of thepatient.
 15. The method according to claim 13, and comprising processingthe photoplethysmograph signal in order to detect an artifact that ischaracteristic of motion of the patient.
 16. Apparatus for diagnosis,comprising: a sensor, which is configured to be coupled to a body of asleeping patient and to output a photoplethysmograph signal; and aprocessor, which is coupled to process the photoplethysmograph signal inorder to identify apnea episodes which comprise Cheyne-Stokes breathingevents and to distinguish a light sleep stage from a deep sleep stage ofthe patient based on the identified apnea episodes; wherein theprocessor is configured to distinguish a rapid eye movement (REM) sleepstage from a light sleep stage responsively to a characteristic of theCheyne-Stokes breathing events, selected from the group ofcharacteristics consisting of a Cheyne-Stokes wavelength and adesaturation depth.
 17. The apparatus according to claim 16, wherein theprocessor is configured to process the photoplethysmograph signal inorder to measure at least one of: a vasomodulation in the body and aheart rate of the patient.
 18. The apparatus according to claim 16,wherein the processor is configured to process the photoplethysmographsignal in order to detect an artifact that is characteristic of motionof the patient.
 19. A computer software product, comprising acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a computer, cause the computer to receive aphotoplethysmograph signal from a body of a sleeping patient, and toprocess the photoplethysmograph signal in order to identify apneaepisodes which comprise Cheyne-Stokes breathing events and todistinguish a light sleep stage from a deep sleep stage of the patientbased on the identified apnea episodes; wherein the instructions causethe computer to distinguish a rapid eye movement (REM) sleep stage froma light sleep stage responsively to a characteristic of theCheyne-Stokes breathing events, selected from the group ofcharacteristics consisting of a Cheyne-Stokes wavelength and adesaturation depth.
 20. The product according to claim 19, wherein theinstructions cause the computer to process the photoplethysmographsignal in order to measure at least one of: a vasomodulation in the bodyand a heart rate of the patient.
 21. The product according to claim 19,wherein the instructions cause the computer to process thephotoplethysmograph signal in order to detect an artifact that ischaracteristic of motion of the patient.